Competitive economies motivate business managers and other users to obtain maximum value from their investments for Corporate Performance Management (CPM) tools, such as Business Intelligence (BI) tools, that are used to manage business oriented data and metadata. These CPM tools provide authored reports or authored drill-through targets to link content together. Users often encounter problems in finding important reports or relevant data or drilling to related content if it was not previously authored.
Traditional search technologies often provide incomplete or irrelevant results in the CPM environments. There are metadata search tools that run against relational databases. They can fail to find relevant data since they only search databases and do not leverage a customer's investment in CPM tools and applications. Relying on authored drill-through targets can also be problematic as new cube, reports, metrics or plans are added since new drill targets are not always kept up-to-date. Users can have difficulties moving seamlessly between CPM tools or applications, particularly when CPM applications are created by different individuals or departments.
It is therefore desirable to provide a mechanism that allows more effective searches of business oriented metadata context.
There exist search engines that use a full-text index combined with statistical methods to create ordered search results. An example of such a search engine is page ranking that is described in U.S. Pat. No. 6,526,440 issued to Bharat. However, these search engines are not sufficient to search complex data like business oriented metadata since they rely on ranking algorithms that work with data found primarily in the Global Internet and not inside a business.
Many existing search engines provide basic full-text search features. Many of these engines use a combination of dictionary, thesaurus and taxonomy components to remove query ambiguities and to provide very limited exemplar term functions.
In those search engines, creation of an exemplar term database is a manual process or an automated process based on advanced linguistic analysis. Each of these systems is potentially expensive to maintain and can produce inconsistent results.
It is therefore desirable to provide a system that manages exemplar terms for business oriented metadata content automatically without the need for manual classification or complicated and potentially inaccurate linguistic analysis.